Nonlinear association of methylmalonic acid with chronic obstructive pulmonary disease and the mediating role of systemic inflammation: A population-based study
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Jun Wang1, Huihui Jiang2, Yan Wang3
1Department of Preventive Medicine, Affiliated Hospital of Shandong University of Traditional Chinese Medicine, Jinan, Shandong, China.
Background:
Methylmalonic acid (MMA), a sensitive biomarker of mitochondrial dysfunction, has been associated with various chronic diseases, but its role in chronic obstructive pulmonary disease (COPD) remains unclear. This study aimed to investigate the association between serum MMA and COPD and the statistical mediation of systemic inflammation.
Methods:
Based on NHANES 2013-2014 data, 5139 adults were included. Multivariable logistic regression was used to analyze the association between MMA and COPD. Restricted cubic spline (RCS) assessed non-linear relationships, and statistical mediation analysis examined the mediating effects of inflammatory indices. Subgroup and sensitivity analysis were used to verify the stability of the results.
Results:
A significant inverted L-shaped non-linear association between MMA and COPD was identified (P for non-linearity = 0.038), with an inflection point at 135 nmol/L (ln-MMA = 4.91). Below this threshold, each one-unit increase in ln-MMA was associated with a 4.59-fold increased odds ratio of COPD (OR = 4.59, 95%CI: 1.54-13.65), whereas the association disappeared above the threshold. Systemic inflammation partially statistically mediated this association (3.7%-10.2%), with monocyte-to-lymphocyte ratio (MLR) and systemic inflammatory response index (SIRI) showing the strongest mediating effects. The results of subgroup analysis and sensitivity analysis were stable.
Conclusions:
MMA was nonlinearly associated with COPD prevalence, systemic inflammation statistically accounts for part of this association. MMA may serve as a candidate biomarker for COPD prevalence in future prospective studies, although temporal ordering cannot be established from this cross-sectional analysis.
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